In the present project, statistical and epidemiologic methods are developed to facilitate identification of genetic determinants of complex traits. Theoretical and simulation-based approaches are used to evaluate the properties of these methods in various situations. The methods are applied to data generated in other projects, particularly "Genetic Epidemiology of Diabetes and Obesity" (DK069028-22) to help assess the genetics of type 2 diabetes and related traits. Methods have been developed for assessing whether an association can account for a linkage result. A method that combines general association statistics (which are powerful but potentially confounded by population stratification) with within-family association tests (which are less powerful but robust to stratification) was developed in an attempt to retain the desirable characteristics of both tests. This test has been applied to a genome-wide association study of young-onset type diabetes involving 1,000,000 makers. Several regions with potential diabetes-susceptibility genes were identified. Marker panels for mapping by admixture linkage disequilibrium in populations of mixed European and Amerindian origin have also been developed. Individual admixture estimates derived from this panel are being used to account for population structure in ongoing mapping studies. In addition current methods for imputing untyped markers from sets densely typed markers are being assessed to determine their applicability to American Indian populations. Current efforts involve additional application of the tests that combine evidence from general association, within-family association and linkage tests to data from genome-wide association studies and other mapping projects. It is hoped that these methods will facilitate the identification of potentially important variants for further functional study. In addition path analytic methods are being evaluated to examine pleiotropic associations of genetic polymorphisms with diabetes and its quantitative subphenotypes. These are being applied to studies of diabetes and measures of insulin secretion, insulin sensitivity and obesity. Methods are also being developed for analysis of genetic transcription data that account for alternate splicing patterns. Methods for assessment of biomodality of gene expression have been evalauted as have methods for accounting for laboratory artifacts in transcriptome-wide microarray expression studies. These are being applied to data from an exon-specific array applied to skeletal muscle tissue to identify transcription patterns that may predict development of type 2 diabetes.